Tab. REF  gives the comparisons with previous state-of-the-art (SOTA) methods. From the table, we can see that when searching only generators, our EAGAN can find a much better generator than AdversarialNAS [1]} in the same search space, which implies that the evolutionary algorithm can effectively find excellent generators under multiple objectives (IS, FID, and model size). Compared with the existing NAS-GAN works, our discovered generator achieves a highly competitive FID score (10.14) and IS result (8.76\(\pm \) 0.09) on the CIFAR-10 dataset. In terms of IS, there is a certain gap between our method and BigGAN [2]}. The possible reason is that BigGAN introduces category information as input into the generator's architecture, while our generator only receives noise data. As a future work, we can try to improve the GAN performance by incorporating category information into the search space. We also evaluate the transferability of the searched generator on the STL-10 dataset. The results in Tab. REF  show that our searched generator has strong generalization ability as it achieves remarkable results of IS (10.02\(\pm \) 0.11) and FID (23.34).
